{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tips = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x249785f0dd8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.lmplot(x='total_bill', y='tip', data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f/+wkYwlP7Jvr/PzeDRv50OVrMI3aEl5DKSLBwmVVBFioeWbLcpAVauVIOy4D0STxVPnN\nc1yteepIH18+cJyzo15mQ8hncse2dm7bup6Qzyz7HIrBZxo0BH1EghZGER8K0hFDqGnysxzyV7j3\n37JFRLaCVNIy8vnjQ+x9+hhv9UUBMA3Fv7tyDR+9fiNNYX/Zxy+GoM+kMeSbzsRHOmIIi5/FUD22\nlP0dUra36k1UwDLyrb4oe/d38/yJ4dyx917Syt3bO1jXFCr7+IWilKIu4AlvwFrYSlwEWKhpar16\nbCn7O4zE0gzFUmW3jDw7muArPz3Oj16/kNlw1fpGdu3o5LI1DWUduxhMQxEJ+mgIWiWrqhMBFmqa\nWq8eWwwr9GKplFH6aDzN1392km///BRpx5PeTS1h7rmxk+s6m2sms8FnGjSGfUQCVsnnJAIs1DS1\nXj1W6yv0YtBae7HeWHmN0pNph39+6RTfONST63zcUu/nEzds4oNbVtdMZkPYb9EY8hHyl2/DTwRY\nqGlqPcuh1lfohZK0HfrHymuU7riaH75+jq/89Dj90SQAdX6TO6/dwEeuWUewBjIblFLUBzzh9Vvl\nN+8RARZqnlrOw631FfpcVKI9kNaaQ8eH+OL+Y3QPeKaHlqG49Z1r+Z13b6Qx7JvjDuXHMgwaQhaR\noK+iK3ARYEFYALW+Qp+NSrQHevPcGP/7J938vOd87tj7u9r4xPZNrGmsfmZDIJtG5jerEnMWARaE\nBVLLK/Tp0FozHEtzPla+Pginz8f50oFj/Nsv+nPHrtmwgl07OrlkVaRs4xZKfcCiIeSrethDBFgQ\nlhGJtBfrLVd7oJFYmq/97ARP/Pw0dsaWsrO1jt07Otm6samqmQ3ZMuGGkK9ibefnoiABVkpdA2zH\na6D5U631i2WdlSAIJaXcTTETaYd/fLGXRw/1MJ4pVW6LBPjEezbx/stWVTWzwWcaNIS8NLJiyoQr\nwZwCrJS6D/gN4J8yh76ilPoHrfV/LevMBEEoCeVc9Tqu5geHz/KVZ44zGPVCGvUBi9969wY+cvW6\nimQSzMQsZcI1QyEzuxO4WmudAFBKfQ54ERABFoQKMZ9yZ9fVDMVSjJZh1au15tnuIb74dDfHB72c\nZ5+p+LWr1/Fb126gIVSdzIZSlglXgkIE+DgQBBKZfweAt8s1IUEoJUvBp2HfkT7+7PGXiSbtXM+1\nP3v8ZT5/21UzvpZytoJ/48woe/Z380rvCOC5znzgHav4+Hs2sbohWPLxCsE0FA1BHw2hyqaRLZRC\nBDgJHFZK/RAvBvxLwAGl1BcAtNb3lnF+Nc1S+OVeypTTp6GSP/vPff8NzsfSmEphKoV24Xwszee+\n/8aUMcvZCv7UcJyHDhzjJ29eyGzYurGJXTs6ubitvuTjFULAZ9IQtKgvQ5lwJShEgP858yfLvvJM\nZXGxlE1Ylgrl8mmo9M/+2GAMQ5HbQFIKtKs5Njix3Dme8mK9pW4FPxxL8cjBE/yfV87kGm5e3FbP\n7h2dvGtjU0nHKgSlFHV+sybSyBbKnAKstX64EhNZbCxFE5alRrl8Gvbs7ybtOAxGbVKOi9/0qqiq\n9bMv16o3nnL4hxd6+NZzvcQzxjyrG4LctX0T7+tqw6jwirMcbmTVZkYBVko9prW+XSn1KjClRlFr\nfWVZZ1bjLCUTlqVKuXwajvaNMRJLYxgK01DYrmZgLEXaGVvolKelc2UdR/uiKK291a8GV8Pm1rqy\nZDg4ruZ7r57h4YMnGBr3Mhsagha/fd1Gbr1qbcUzG8rpRlZtZlsBfzLz9Q3gU3nHFfDfyzajRcJS\nMWGpFIXETEsRV82/R73fzGUATPZpWMhYKdsFRW4FqBS4Sk8o6Z3r/sWM/+mbu/jU4y8zlrBJ2y6Z\nKABpx+XbL57i2s7mor5HM6G15sBbgzz0dDc9w3EA/JbBr1+zjju3baA+0+vsUPcQjz7Xw5nROGsa\nQtyxrX3GORRz7mTm60a2mPZmZvwo01qfyfz1Yq31ibw/x4Guisyuhtm9o5O0o4mlbLT2vi4mE5ZK\nko2Z9o0lJsRM9x3pK+qcYsdJuxoN+E2DkXiatkiQ+2/ZArCgsXymJ7yuq9Fa42YU0Z85PtdrKfa1\n7uxq469uu4qOlXWgFH5LsbYxQNJ2efCpoxzqHir4ezQTr50a4d5Hf85/fuIwPcNxFHDzltV87RPX\ncs+NnRPE98GnjjI4nqQhaDE4npxxDsWcm8VQioaQj/bmMKsbg/MS34W+jyrJjAKslPq/M+GHS5VS\nr+T9OQa8Urkp1iY7u9q4/5YttEWCE365a/WTtprkx8uV8r76TMWe/d1FnTOfcRpDPlaE/Tz96Zv4\n5q7r2NnVtuCxLlnVQEudH8tUOFpjmYqWOj+bVzUU9FrmM/7OrjYaQz7WN4XY2FxHXcBHyGdiGYpH\nn+sp+Hs0mZODMf7iO69x76M/5/DpUQCu62zmoY9t5T/efCmtkcCE8x99rgfL8PrzKdSscyjmXJ9p\n0FIfYENzmJX1gXmXCpfifVRJZgtBfAP4PvD/Ap/JOz6mtV74R+4SYLGZsFSLQuLlpYipF3qPhY6V\ntaBc3WhNa0E51/3nM34sZXN8cHxKy/Ogz+DsaLygeeczGE3yyMETfPfVM7mQxqWrI+ze0ck721fM\neN2Z0TgNBc6hkHNDfq9oIj+UtxAW297MjK9aaz0CjOBVwgnCvCkkXl6KmHqh91joWHNZUM51/2LG\nz89wWN0QYnA8OaEleyLtsrqhcFvHWMrmsed6eez5HhKZmPWaxiD33NjBey9pnXOTa00Rc5jx3MYQ\nkaCvLKbni21vZmnkcgg1TSHx8lLE1Au9RynG2tnVxjd3XTchtFHo/QsdP55y6B2O59LL7tjWju1q\n4mkHjffVdjV3bGufc7624/Ltl07x0S8d4pFnT5CwXRpDPv7ofRfz1Y9vY+elbQVlGBQzh8nnJmwH\nV8O977uY1kigLNkUi21vRpW742kp2Lp1q37++eerPQ1hAWR3pmczLS/knFKMU6qxFjKP2f5/Ng+H\nbFbB2dE4qwvIKtBa85M3B/jSgWOcOu89+gcsg9vetZ47trXPy6immDkc6h7isRd6ODeaoL0pzO+/\n96Kyh+3K/bMtkILy5USABaGGKKWHw8u959m7v5s3znj5yYaCD12+ho/dsJGV9YE5rl4YWVOchuDi\nr1abJwUJcO36tAnCMqKU1WzHBsb54tPdPJuX7nXDRS3cc2MHG1vqirpXsXm8WVOcyBKqVisnIsDC\nkmIxJeFnGU/aDEZTC/Zw6B9L8vAzx3ny8NlcZsM71kTYveMirljfWPT9snm8lqEm5PF+ks1TRHix\nm+JUCxFgYcmw2AySHFczGE0STdrzuj67Oj01EsNAMRRLkXY85V3fFOLu7R3cuHnlvAUxP48XLlQT\nPvpcT06Aa6W32mJFBFhYMiwmg6TxpM1ANJlzFyuWQ91D/O2P3ySRdhlNpHMr3vqAxV3bO/jwFasX\nHAKYKY/33GicFWH/kjLFqRYiwMKSYTEk4S901Qvgas3/+snb9EdTOQFXCiIBi43Nddz6zrUlmevk\nPF6lFCnbZWNLHc11/pKMsdyRjy9hydDeFM7ZJmappST8WMrm1HB8QeL74slh/uDrL3JiKJYT38aQ\nRUdzmFUNAfqjiTnuUDjZPN6k7WAZCtv1jIB+/70XlWyM5Y6sgIUlQ7ZEOJaypy0RrhaOqxkcTxJN\nzF943+6P8sX93Rw6Ppw7FvIZrIoEcwUN8bRTVFXcbCileP87VtFS7+fLPz1e7ZzaJYsIsLBkmKtE\nuBr84NUz/O/93ZweKd6OEeDcaIKvPnOcfz18LmfKfcW6BrZftJJvv3waR2uvyiztFlwVNxtZ0/PG\nTG+1X9qyml/asnpB9xRmRgoxBKEMuK7miZ+f5oEfHMEyFEGfkRPJT940NY1rMmOJNN881MM/vtib\ny2zY0Bzmnhs7uOGiFpRSRVfFzYbPNHL5u8YiampZw0ghhiBUg1jKZmAsxVefOT5nGtdkUrbLt39+\niq//7CRjmZBFc52f37thIx+6fM2Ejr/XdjYv2IzdbxmsCPup85uSv1sFRIAFoUS4rmYgL9ZbjHWj\nqzU/eqOPLx84Rt9YEoCw3+Q3t7Vz27vWT3AUKwWSv1sbiAALS5JKV8RlV7351WyFWjc+d3yIvfu7\nebt/HPDisLdctZbfuW4DTeHSpXuZhqI+4JnUS/5ubSACLCw5KlkRN3nVm88d29p58KmjxNPOhBhw\ndqPszXNjfHF/Ny+cPJ+7Zuclrdy1vYN1TaXJZoBMfDfkNbWU+G5tIQIsANXzUCj1uPuO9HHvoy8R\nSzkELIOV9QEaQr6yVMRNt+rN59rOZj7J5ikbZRtawvy3773Bj9640Kfsne2N7NrRSdfqhpLNL+jz\nuk3Mx3JSqAySBSFMWDHm58+Wu8ddqcfN3u/U+RiWoUArXDRrG0NEghYj8TRPf/qmBc97tlXvbIzE\n03zjZyf59s9P5TIbOlbWcc+NHby7o7lkm2AS360JJAtCKIxqeSiUetzs/YKWie1q73HbhYFoEstU\nJamIy656n3lroGCbxmTa4Z9eOsU3Dp1kPOlV6q2s9/Px93TwwXesmpDZMF+y8d2GkG/eDS2FyiMC\nLJTcQ6HQsEKpx83erzUS4PT5BC4alCZp6wVXxOV7OBRq0+i4mh++fo6v/PQ4/VEvs6HOb3LntRv4\n9WvWESjBCtVvXYjvShrZ4kMEWChpI8NiNsBK3UAxe79I0MfaFZ4/bsJ2qfNbCwqnjCXSDI1fML6Z\ny6ZRa83Pjg3x0NPH6B7wMht8puLWd67lt9+9kcZJHzrzoS5g0RD0EfJLmGExI88qQkkbGeaHFZTy\nvvpMxZ793WUdd/L96gMWqxuDrFsR5gt3XD0v8U07LmdG4vSPTbSNPDMaJ+ib+KuTze/9xdkx/vQf\nXuE//fNrOfF9f1cbX/34Nv5g58ULEl+lvDLh9U1hVjUERXyXALIJJwDFNc2cLbSw/YGnWBHyTXgc\nHo2nODuapDUSIBKw0FoTTTm0N4W5vrOZg91DJfNuKEVDRq01o3GboViK6X4//uRbL0/J7x1LpImn\nXc7nNdK8ZsMKdu3o5JJVkXm/HpA2P4sUacoplI5CMxbu3PvshLDCWCJN73Acy1SsigQ4dd6zS1y3\nIohlGhXJtiiGRNpripmyZ24PlB8DtkxF/1iSaPKCDeZFrXXs2tHJ1o1NC4rLSnx3UVPbAqyUMoHn\ngVNa61+Z7dylJMCVzLctdqzs+Uf7xkjZLj5TccmqBnbv6GTP/u6csI7G0wxEk8RTDspQNIV9bG6L\n5EIH+UL9Vl/Uu5dleKKmwMTrIdbZWk8sZdMWCfLNXdeV5DUUc+0XfvQmDx04xnjKIewz+O13b+D2\nbRsKuveBNwf4nz95m3OjiZxLWVskwF3bO3j/ZW0Y8xRMpRR1frNsaWSLsWfeIqXmBfhPgK1Aw3IR\n4Erm2xY7Vvb8tOMwMJbKvX1a6vz4LZPxZJo1jSHGEjanR+JoV2Nn3jo+Q7Ey4sdnmtx/yxbggiXk\nmZE4GrCUQTKv1bqhYMvaRrTWM+bnLuT7Nde1X/jRmzz41FsYChQaV4Or4WPXbeSjN2ya8b6Oq3ny\ntbN89eBxBqMpACJBi9+6dgO/dvW6nDdvsViGQSRolTXMUK1872VKQQJclYCSUmo98GHgoWqMXy2K\n2aCq9FjZ80fjNoahsAwDA8VYwsZnKtKOJp55PDdQZKXUUGAY3nXZ++/sauObu67j6U/fRNhvYSiF\nYaiM2E1ktqyHhXy/5rr2oQPHUGhMpVDKwDQMDAWPvdA77f201vz0rQHufvh5/r8fvslgNIXPVPzm\n1vX8/V3X8pvb2uclvgGfSVtDkPbmEE11/rLGeCv5/hMKo1ppaH8L/Edgxt0JpdQuYBfAhg2FPRbW\nOpXsWVbsWNnzU46bKwxQClKOS8hn4re8eG3SdjENck0gLcOYcN7k+/stg3jKwdUay1CknGwPM8VA\nNMHQeJrzsRR37n12yuPwQr5fc10bTdpYBuQ//ynFlJZGAK+fHmXP/rd59dSodx7wwS2r+L0bNrGq\nITjnXKajGtVqi6Fn3nKj4itgpdSvAH1a6xdmO09rvVdrvVVrvbW1tbVCsysvlexZVuxY2fP9pkE2\nKqU1+E2DeNphc1uE+2/ZQtifqTJTYBkK01ATzpt8/81tEVZG/FiGQgNBy8BveqvhofE0TWEfaxpD\nuXzhfUf6psyp0NdQyOtfuyKsI4mlAAAgAElEQVTEmZE4IZ/J5IbEWjMhs6FnKMZfPnGYP/rmSznx\n3bapib0ffRefvrmraPE1lKIx5KO9OUxbQ7DipcK13jNvOVKNEMR7gFuUUseBR4GblFJ/X4V5VJxS\n572Wcqzs+Q0hC9fVXgNGNJGglbtuZ1cbX7jjatatCNMWCaAU3nmud91099+9oxOfabK6McilqyKs\nawqxujHERSvrWN8UojUSnPFxeCHfr+muTaZdPnL1OuIph9vftR5Xg+O6uNrNfIXb37WeofEUf/uj\no3z8q8+x/+gAAJvb6vn8bVfywK9fyUVt9UX9LHymQUtdgA3NYVrqA1UrFa7k+08ojKqmoSmldgJ/\ntlw24aA0earlGmtyFoTfVGzOZEHkX5c779woKUfjt4xcFsRMG3yT5/Hn33ltSr7wdBtyC/l+5Xb8\nh8Zpawhy+7sm+jV87ZnjPPZCL/G0Q8hn8mvvXItpGnzr+R4SaS/KvbohyF3bO3hfV2vRmQ2BjBtZ\nfQ25kVXy/bfMqe0sCFieArwUyE/fqvOb3L29g3s/cElR95icLwzMmZJWLFlBH46lpy2oyGI7Lt97\n7SwPP3Oc4ZhXSNEQtPid6zZyy1Vri95cq8uYnosb2bKm9t3QtNb7gH3VnINQHPnpW5bhxRAffOot\ngKJEuBwt5PNzXNetCPEb71rPNRubZjxfa83Tbw3w0NPH6B322gT5LYPbrlnHHdduKGrlaihFJChu\nZEJxSCWcABSeoH/lX/6AeNrBMi6IjO16GRCv/OX/Na8xS/E4nM1xtQwyecv2rB2IXzs1wv/+STev\nn/E21wwFN29Zzcdu2ERrJFDwuNJNWJiB2l8BC7VBMQ5m4ymHyU/khvKOF8vOrraSxR/37O/GMsBn\nmriunrED8YnBcR56+hg/fXswd+y6zmbuubGTjpV1BY8X8ps0BCvTbUKq15YuIsBCUcbodX5P2PIX\ne672jlcLrTXHB8epD5gTYr35HYgHo0kePniC7716Jpd+1rXa2zi8qn1FQeMYSlEf9Gwg51vxViyV\n7G8nVB4RYKGoBP27t3fw4FNvYbsuhiJXwnv39o5KTXfCirDOb2I7LgPRJINRaI0EqMt8kCTSLq31\nQb7802M8/nwviYzBzroVIe7a3sF7L1lZkMmNZRg0hCwiQV9JulcUQ7W6lQiVQQRYKMoYPbvRttAs\niPmSH+tFa97qiwLQELIYjducHUmwqiGAoRSjCZuh8RSvnh4BYEXIx0ev38ivXLmmoI0yn2mwIuyl\nkVXLjaxnOIapoLs/Sspx8ZsGK+v9Ur22RBABForOSLj3A5dUTHAns2d/N2Ym1ntmJOFtfGlvtbuq\nIUh/NMG50SSmYZDKmP8ELYPf2Lqe27e2FxSzDfm9/N38D6RqEQlYHO2LYmaqDm1Xc+p8gs1FFoMI\ntUn132FCSVjIRs3Orjbu54KDWZ3fxG8a/Ok//JxYysmUHis6V9bxoctXc7B7aMZxirGADJgGzXU+\nUCp37iu952dcXU+O9aYdFzQ4GtKuxokmAYWjNY7jhUh++Yo1fOz6jbTUz53ZUB+waAz7CFi1k7+b\ni2lnQ9t60vElyHLadJQ0tCVAKW0Gs/dK2Q59Y8k80x1wXUDBqoYALXWBKeMUYwGJ1mSKzWit99EQ\n8jMQTTIat7EyXhHZ+PInb7qYXe+9iP6xJH/8jZdy3SiODURz95jMey5u4Z7tnWxomd3nQGXydxtr\nNH93+wNPYSoYiKYmhCBczbQWnoudJWSZWbt2lEJpKaXNYPZeYwk7Z8qj8IRQZ0RxNG5PO04hFpBe\nAYeBoy+8QwfH0xmjdxvw/t9Qhue0hmbv092cPh8n7bjcsa0d2/WsMWdaOqxtDPLZWy+fIr6Huof4\nk2+9zJ1ffJY/fexlXj89yobmMCur6M8wF+1NYSzToLO1nq7VDXS21mOZxpI10Flulpm1+a4TAG81\ncOfeZ9n+wFPcuffZCU5h+fQMxya4eMH8bQbfPDfK2ZEE46k8gVOeU1hWkFN5xur548w1j/HUhfS1\n/AcvN+8pO19UtfZc12J5OcbXdjaz+8ZOkrbL5K5BflOxpsGPO81TXbaN0FAsSXPYz0g8xX//wS94\n+s3+Qr4tVWO5GeiU8r28GBABrlGyj2J9Y4kJ+Z/TiXCpbAb3HekjmnRIZeKnWbT2vHKziQD+vNVi\n/jizzSM776StSdoTz8mOpTJ/tNbeHzxxzv5CpmyXx1/o5a9/9CZ9Y8nctW2RAJtb69jUUodlmqxu\nCE15bY+90EPQZ9AY8kzP6wK+RbGy2tnVxv23bKEtEmQknqYtElyMj+MFs9wsM2UTrkYpJv+zVL4K\ne/Z301znYzCaxlB6gl+uobwYsFJeypfWeso4M83j+s5m7nviMA1Bk+GYPcWHt6XORyxl0xCyGInb\n2K6Lyqy6XQ2/cc06fvzGOf7nvrdzZjmGgivXNXJmNOF9IGTM1G1Xc8e2diDTXy3gZTT0jSUXrRl5\nKSsGa51yeITUMiLANUoxxRGTsxjm66vQMxyjpS5AwDLpH0vipp2cWCoUQZ+BYWjGkw5pJzHFgnKm\neWQ/TNY31eEz4gyMp3C1J6JrGz0/4JX1Af7gvRfx2qmRCRaR2y9u4afdQxx99mRuno0hizq/xbmx\nJL+8ZTUv9YxwdjTO6oYQd2xr590XtRAJWqwI+XItforJdRaqR6ney4sFyYKoUSph11jomH7TYDzl\n5HamB6JJhmNpIkFrVh/gLNsfeGpG79+ffOp9DMVSjMbTE6759oun+MozxxlL2rljIZ/BqkgwVwYc\nTzu01AX469+8CvBKhRtCPhpDUyvWltDuurA4EDOexUwpH8UKzau8vrOZv9v3No6rCVhel16/ZeIz\ndC4cMhpPMzjudQOOJe05vQn2HeljNJ7m7EiCgGWwsj5AQ8hHPO2wujFI73Ac272wm3Z2NMFfPfkL\nXuo5nzsWyLS0bwpP9GDIej1YhkFjaHZHsuW2shIWByLANUqpBKNQM5d9R/p4/MVTNNf5GImlSdgO\ndkzzhzs38NgLvblwSLYrsjK84ofpYtNZwX/z3CjRpFdQofCyJ06PxEmkbUzD4Ne3r8+J72g8zTcO\nneSfXzpFOtO40296oYk6v8mJoRgD4ynqAxfCMknbpb05THtzqKBS4WrFUpdTYYFQHCLANUwpBGO2\nzbzs//cMxxiNp6kLmKysD7Ky3ms2GUvZHOwemhA/TTkuprrQiBMmxqbzBT+asEllOin7TIXSGltD\nNOnwFx++lGs7m0nZLv/80im+/rOTRDPhBkPByno/jcELYYuV9X7Ojia92LDf9KrgUPzhzouL9mmo\npCCKm5kwGyLAS5yZNvOOnhudIAxnRxLEUw4ByyQS9OXO6x2O8dlbL8+FQ/ym57GgUDnj8vzNrKzg\n244m6VzYX0g7GsuA1Q0BXA1bO5r419fP8eUDx3IpZWG/yZ3XtvNc9zDD8dQEYTUNg86VdbTUBTg9\nEl/QE8GnHn+ZsYSXbTEwluRTj7/MX912VVkEsdAPQFkZL09EgJc4M+3+pxxNY54wBCxPWPszYtg/\nliRhOzlrx/tv2cLnvv8GSdvBdsFvMm1hQFbwj42Mezm9eXNRKPqjSdqb6tj9tRd4u38c8Nrb33LV\nWn7nug2sCPvZ3BrhwaeOEk87BH0GqYyQ//mH37FggXrgySMMx9KYhsIyDbSG4ViaB548Mi8xn0tA\nC/0AlJXx8kQEeIkxWRSu72zm8RdPTdnM81vGhIqjlfWBTHzW4dRwHJSXVRD2m9z3xGFuu2YdsbTL\nxpY6bMfl3FiS3vMJLmmr5y8+3JUTjazgJ20HU4GdV86stcZ2oXtgPDfu+y5t5RPbO1i34kLxxLWd\nzfwHtZnHXujl7Eic9ua6kq0OuwfGMTKvDby8Zq30hDkVQqGhhUI/AMXnd3kiAryEyDfSGUt43rgv\nnhzmmvZGDp8ZYyxhYxgKA88IZyCayl3reTQo3EzVm5mphMiGB/7mR0e9OC6e+5irvUKNN86O8Qdf\nf5FwwKS1PsBYIs2p84kpPg2aC2IMniAbBpwYGOfUUDwnwD7ToCHkY2Uk489QYR/e6Va1MDVUUGih\nzEzZLJM/AKEyhSHTfUDP5m4303USLikNkge8hLhz77McG4gyOJ7yMhUUpF0Xx4UVIYvxlJPLMJgO\nA0B5lWnn4zba1RNEczayZcQAMxiUTTnfMrxKt4aQj//nly/j5ivWUB+wypqze/Pf/IS3+scxlcpV\n2zlac3FrHZ/50GVTxh2Np9FAY8g3YS6xlM3qhuC0uc2TXcqmaz66Z393xfO8J39fB6JJ+qMp2iL+\nad3tZrpOcqgLQtzQlhs9wzFvlYvCMBQqk60AMJo5Pht+y8BvGgyOpzFQOSEt5J2k8YQ3e810rXvM\nSYcMw8BUiljK5h9fPJVrA19OR6zPfOgyVoR9KMMTXmXAirCPz3zosmnHHUvYRJP2lLmkbLdgz4Kd\nXW18c9d1PP3pm/jmruvY2dVWFZOdya9vLGFjqJnd7Wa6bqk7lFUSEeAlRHtTmKTtTnhqd/UFO8m5\nnuYdrVmVyVLQ6CmeDcXg5F1sKE9888fXZMMQXtgj/9F7LkesQl3ipmNnVxufv+0qrm5vYnVDkKvb\nm/h8JgNiunFt153wWrJz8ZtqQQJaDZOdya8va7o0k7vdTNfNdJ5QPBIDXkLs3tHJiyeHcbTGzDxe\nKzwB1Ey0gJwOv2lgmQZhv9faXWUiuT7TyOTdMqMH73QYeCtiQ5Hz280WXhjKM8txXY1pKOr8Jnfu\nfTaXk2w7Lq2RYO5e+a5qC80emCm/eroNM8swpjwCxNMOm1c15EIJ8y2UqXRhyOTXl00pnMndbqbr\nZjpPKB5ZAS8hdna18Yc7L8JQirTjYipoCnstfxqCFu4s8qmASNAi7Wh+f0cnbQ1BVjUEsEyFRmcy\nBwp/w5jK22QLWgrbhWTaxXHcnJYpwHFdHK3xGYrB8VTOerMuYNIfTdE/lpiyuizn4/B0YYFI0KI+\nYE270p0utFDLTH59kaCVicFbs67il5sncSWRTbglyORNn+xO99Fzo6QcTcr20qCy4Qmf6Ql0dlWX\nbS+0Z3937hq/ZdAc9mM7LsPxNOMpm7StJ0h6yOedE0s7pGwvIpx2IOw3SDvkwiMNAYtoxmS9oyWM\nUoqU405YYQ1EE4wnHRpDvgmry9mMfUrRome6DTNYOh4SM7035npt031fFuv3oEIUtAknAizMSSLt\nMDieIpl2GI6l+NrBE/zLK2dysdGLW+vZtaODrZuagQvt3O95+Hn6o8k5d/qLEdVquMQJwjwQN7Tl\nwL4jfXzu+29wbNDbEOlcWcenb+4qyerkR6+f5X/t66b3fIy2+iCrG4MceGsgt/vfFgnwie0dfOCy\nNgylcsKbLWXuPR/HVNDdH53QUHLy5k0xMcblZtgtLG0kBryI2Xekjz97/GXe6h/PtfE52hflU4+/\nXFRmwGRsx+WJl07xqcdf4ZXeYfpGk7x6eoQfvnGOeNohErT4/fd28sgnruWD71hFwDJpjQRobw7n\nxBcgErA4dT6Bndlos13NqfOJXLpZlmJijMutRY+wtJEV8CJmz/5uokkbU6mcD67SmrHE/EpaHVdz\nPpZiNGHzNz96k5FYekpRRWPIxyOf2EYk6Juy4p1MLryVjXLpScczFGu9uZxa9AhLGxHgRUzPcAzH\n1V7ZcAalvBVsMTmarqs5H08zGk/jas3h0yMcH4xNmzMxEk/z5tkoN1+xekbhzRJNOaxbEWQgmsqF\nIFbXBxhPOVPOFVEVliMiwFVmITX27U1hBqJJbEfjatfL81XgM1RBOZrZja6ReBrH1ZwcivHQ08c4\n8NZA7pzJjmYA/+Pf3qI1EphzntnYbmdrfe5YdsNMEAQR4Kqy0KKC3Ts6+eNvvkginRco0GC7Xifi\nmdBaMxq3OR9P4biaofEUDx88zndfOZOrfvObBrbjTglBBEyVy7uda47l2jCrhDGMmM8IlUAEuIoU\n03o+y2RhWBH2k0jHsTNKGbC8uOzB7iHunXRtVnhH4mls1yWWsnnsuV4ee6EnJ+JrGoPctb2DsM/k\nr/71F7k28FmypjSFhDjK0YetEh0mpIuFUCkkD7iKzJb/+tlbL5/WFvG+Jw4zlkgxErdzq9WwpVDG\nhbLSsN/gfMzObcxtag7xx++/hCvWN+K4Gttx+e6rZ3jk4ImcwAYsA601aUcT9pt89LoNBH0mDz71\nFjrTQt77Y1AfMLFdTUPIV/HVYSXygCXXuDIs8acMKcSodWb6RfcZiljanWL/V+c3OTsaZzhmT7mX\nmfFbsF2NnVFmXybJMGv5+B8/eCkJx+VLB47ROxwHPOG9dFU9r54aRSkmGPC0hC18ljmtveXqhgAr\n62e2MJxMqX7Zyl0JV6kxljvLwOJS7ChrnZnyX5VS0/oddA+MMxL3xFdNchdzMhtwE1zIDMP7oxRj\nyTSf/d4b/Jd/eZ3e4TiGgl++YjWPfOJa3u4fz4mv4sJ9B2M2ActgbWMIy1Q4WnurYaA1EizYiyH7\ny5b1esg+0s8nV7m9KVywDeR8qcQYyx2xuPQQAa4iMxUVjCXtae3/gFktIm1nqt2Oq70Vse1ALJP+\ndX1nC1/83a382QcvZe2KEPG0k3NOm/y5fW4sSUPIR2drPV2rG1AKQv7irAlL+ctWCWMYMZ8pP2Jx\n6SGbcFVmuvzX9v3Tl+Z2tIT5RV/USzebpLQK6FxZT89wjHhmQy3tuBMEO+w3+W+/djlXrl+BaSia\n6vxEAp7b12jCnuIXrBQ5Ico+JlqGQSQ48W0z1+pwpsaU8/llK8fGXjXGWO6IxaWHCHANkp++lW2A\nmXY0l7TVc92mJg4eG56y0m0IWmg0Yb+RE+B88a3zm/z5L1/GVe1NNIZ8rAj5cpt0d2/v4K9/dHSK\nX3Bj0GJNY4gVYT9H+8ZI2S4+0+sirJSXqpadm9802Hekr2Cf3YX8slWiaEMKQ8qLeHp4SAiiBsmG\nJnyGovd8HDSsXxEk5bicGkly65WrqQ+YGMoT1g90tbKxpY6zo0nOxyfGLg0FHc0h/uLD7+Cmd6xi\nfVOI5jp/TnwB7v3AJVzf0TThurDPoCHk59M3d7F7Rydhv0VrJMCmlnqawj76x5L0DE+c20xxXXmk\nFyYjnh4esgKuUXZ2tbFnfzebXJ1bObpaYztpTgwleOKPtueO/eQX/Tx04BhjCW+DLmgZ3L61ndu3\nrSfst/BbBi11gSmxW7jgpna0P4o/0/XY1hpHw23XrGNnVxt37n12Qr5yayTI+XgaNGxeFcnda6Yc\n5uwj/ee+/wZH+6KA59omLG/kKUMEuKbJxk5drXFcjet6xuhnR+Mc6h7ii093c2Iolks7MxR8+Io1\n/O71G2mpD+TivA0zeDZ84Udv8nf73vaM0gHDBFC0N4UwDZUr5pguhuu4eoqpzlxx3VjaZX1TKPfI\nKcUNwnJHBLiGWdsY4txonIBlEk3aDMdSpGwX01D8+XdeywkvePm8f7TzYj581RoA6oMWLXWeCE+X\ngwvwd/vexs2IqAZsR2OZ0D+WpGNlXU5Mp4vhmoYCPXHXbra4brFVf0s8SV8QAIkB1yTjSZtT5+N8\n5Op1pBzN0HiSvrEEKdvLakg5F4otgj6D9qYQbZEAPz7Sh880WNMYoi0SzInvdDm4n/v+GzkntfwO\n8o6rSTnuBDGdLoZbH7CIBKfvlTYdxaQdlTJvWBBqGRHgGiHrxdszFOPcaIJk2uHazmY+edNmYikH\n2/WKLfIf+v2mon2F90gf9Bn0jSW8R/y8WO9MObjHBmOZ8mNydpYaL3PCVGqCmE63YfL5267ir267\nquBNlGKKGyRJX1guSAiiyiRth9G4TTRpT4mppmyXE8Mx4vYFTzLT8Fas2tW42quaMwxFynbZ2FI3\noXwWLsRvxxJp+seSpBwXn+G1g4+EfV6ZsVL4DI3teiLcMU1bo5k2TAoNCxSTdlTKvGFBqGVEgKtE\nLOW5ksWnMSd3teapI318+cBxzo4mAK/QornOT1PYRzztcHYkgWkoTMPrKGy7TCtm7U1hjg9GGYx6\nubumoUhnao5tV9NS52csYZO0FX5L8Yc7L+LeD1xS8tdbTHFDrSTpSxxaKDcVN+NRSrUDjwCrARfY\nq7V+cLZratmMZ7Zf0sn/t+vGDrZ2NDMST5OyXQ51D/G3P36Ts6PJ3P2Cpudslv+43hCwUIYXchhL\n2KRsPcWnN0s2npv1dYCJYQtLed90y1DoTEjDnlTfbBmwuS0yZRVcqCDlnxcJWGitiaacgkUsm53h\nuJqA5VXe+S0zF+LYd6SPB548QvfAOK7WWIYi7De5ZFVDUSI5189uLrOY7PUXilRU0XMQliy16Yam\nlFoDrNFav6iUigAvAL+qtX59pmtqVYB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IULVEKjvu6sYgp88nciY958YStEWCJVlBSXyy\nMkgWibAoBPjEYIxURnwvX9vA7vd2smVt46zXnBmN0zDJQjLkNxkYS8wqvlCYAFVLpLLjKqVYu8LL\n/kjaLkqrkhVBSHxSECpD6XadysyG5jCfvXULD97xzjnFF2BNQ4hE1mhXeR66jqtpb547F7i9KZzr\ngpFlsgAVck45yB83EvRM2De21HHNhqaSraZ27+jM+Qtr7X2V+KQglJ5FIcCrGoJ86WNbec/FhaeV\n3bGtHdvVJG0Hn6FI2oVvchQiQNUSqUqMu7Orjftv2UJbJMhIPE1bJCglxoJQBpasF4RpKF4/Pcoj\nB0/Ma5Mjm+Ew27WFnPP/t3dvIVZVcRzHvz9nLE0l8xaRxnQxMCQnMJG0MMkSki6UGF0wCCLqwSQR\n60UKgiQoH6SHqEiiLEmtKIrELM1A85qGRUQWoTlpiVooqP8e9hoaTl5mnDOz2nt+HzicvfdZnr3+\nsP3NmjXnrN0Vcp3XzNqtXSPFSgbw2a7hYGZWJz1vMZ7GXr0Y3P+cdq1yZmaWW2WSqn+fRgb386jX\nzMqj9AF8Nuv1mpn9H5Q6tU51lwozszIoZQB71GtmVVC6BPMnHMysKkoTwA29xJD+5/oTDmZWGaVI\nswYVq3x51GtmVVKKryI3Nsjha2aVU4oANjOrIgewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYz\ny8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDIpxW3pJf0O/Jy7H11kCLAvdye6UNXrg+rX\n6Po6bl9ETD1To1IEcJVJ2hgRY3P3o6tUvT6ofo2ur+t4CsLMLBMHsJlZJg7g/F7O3YEuVvX6oPo1\nur4u4jlgM7NMPAI2M8vEAWxmlokDuBtJek1Si6QdbY4NkrRS0g/p+YKcfewMSSMkrZa0U9K3kmal\n45WoUVIfSRskbUv1PZ2OXyppfarvHUnn5O5rZ0hqkLRF0odpvzL1SdolabukrZI2pmPZrk8HcPd6\nHaj9cPY8YFVEjARWpf2yOgY8ERGjgPHAY5Kuojo1HgUmR8QYoBmYKmk8sAB4MdX3J/BQxj7Wwyxg\nZ5v9qtV3Y0Q0t/nsb7br0wHcjSJiDfBHzeHbgcVpezFwR7d2qo4iYk9EbE7bhyj+E19MRWqMwuG0\n2zs9ApgMvJuOl7Y+AEnDgVuBV9K+qFB9p5Dt+nQA53dhROyBIsCAYZn7UxeSmoBrgPVUqMb06/lW\noAVYCfwIHIiIY6nJrxQ/dMpqITAXOJH2B1Ot+gL4VNImSQ+nY9muz8buOpH1HJL6A8uAxyPiYDGI\nqoaIOA40SxoIrABGnaxZ9/aqPiRNA1oiYpOkSa2HT9K0lPUlEyJit6RhwEpJ3+XsjEfA+e2VdBFA\nem7J3J9OkdSbInzfjIjl6XClagSIiAPA5xRz3QMltQ5mhgO7c/WrkyYAt0naBbxNMfWwkOrUR0Ts\nTs8tFD9Ax5Hx+nQA5/cBMDNtzwTez9iXTknzha8COyPihTYvVaJGSUPTyBdJfYGbKOa5VwN3p2al\nrS8inoyI4RHRBNwDfBYR91GR+iT1kzSgdRu4GdhBxuvT34TrRpKWAJMolr/bC8wH3gOWApcAvwDT\nI6L2D3WlIGkisBbYzr9ziE9RzAOXvkZJV1P8kaaBYvCyNCKekXQZxYhxELAFuD8ijubraeelKYg5\nETGtKvWlOlak3UbgrYh4VtJgMl2fDmAzs0w8BWFmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzcwy\ncQBbKUgaKOnRM7RpknRvO96rqe2SoCd5/UFJi07x2le17yFpUuvSjWYd4QC2shgInDaAgSbgjAHc\nGRFxXVe+v/UsDmAri+eAy9NC2s+nx460uPaMNm2uT21mp1HqWkmb06Mj4TlC0ieSvpc0v/WgpMOn\n+0dmHeHV0Kws5gGjI6JZ0l3AI8AYiq91fy1pTWozJyKmAUg6D5gSEUckjQSWAGNP/vb/MQ4YDfyd\n3v+jiNhY35Ksp3MAWxlNBJakpSH3SvoCuBY4WNOuN7BIUjNwHLiyA+dYGRH7ASQtT+d0AFtdOYCt\njNq7wPBsikWPxlBMtx3pwDlqF0nxoilWd54DtrI4BAxI22uAGenuFEOBG4ANNW0Azgf2RMQJ4AGK\nVczaa0q6WWNfilvUrOtsAWa1PAK2UoiI/ZLWpY9+fQx8A2yjGJnOjYjfJO0HjknaRnED1JeAZZKm\nU6xp+1cHTvkl8AZwBcWyhZ5+sLrzcpRmZpl4CsLMLBNPQViPJekWYEHN4Z8i4s4c/bGex1MQZmaZ\neArCzCwTB7CZWSYOYDOzTBzAZmaZ/AP4rBzle4Es2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x249785f0ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OrtsVwz9czkOhm4rmNKMb/iJeDzNclTPsH6NVx9hObYHxZpkDnebVNtpSqd51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\nABMRBeT/A1LvOMgjmnT1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24978850780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='total_bill', y='tip', data=tips, fit_reg=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Mg/yru1p7M6LT6uOsvWIJsZBXVSFgKCIBA0MptIaQ6f0qDbZBdtGMClwNm/Ub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\nERG1ksQUERG1ksQUERG1ksQUERG18g+qqY/2tBrTFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2497851af28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex', fit_reg=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      "total_bill    244 non-null float64\n",
      "tip           244 non-null float64\n",
      "sex           244 non-null category\n",
      "smoker        244 non-null category\n",
      "day           244 non-null category\n",
      "time          244 non-null category\n",
      "size          244 non-null int64\n",
      "dtypes: category(4), float64(2), int64(1)\n",
      "memory usage: 7.2 KB\n"
     ]
    }
   ],
   "source": [
    "tips.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BvpTyliTTtdYP3ulxtdYna637a637d+7c2VB03Gy9q3fW83grg6B5cjC9wj6C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\nObR3KifSKei8MDObB7Q6ANZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2497855e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='total_bill', y='tip', data=tips,\n",
    "           hue='sex', col='day', row='smoker', fit_reg=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
